| With the development of algorithm technology,algorithm-recommended mobile new media was born in a completely new and surprising way.After the initial crazy fascination with algorithm technology,the audience began to calmly face this technology boom.Relevant data show that the algorithm-recommended mobile new media has begun to show a slowdown or even negative growth in user growth.However,all aspects of human life in today’s society are undergoing digitization,and the role and power of intelligent algorithms based on real social data as factual decision makers and arbiters have received attention.There are many users in the academia who have weakened their enthusiasm for social media,but there are few studies on users’ use and impact of algorithm-recommended mobile new media.The literature review combs the relevant research on algorithm-recommended mobile new media and finds that algorithm-recommended mobile new media with intelligent algorithms has greatly changed the traditional centralized distribution method,which not only solves the contradiction between massive social information and user personalized needs,but also To a certain extent,it affects the user’s contact with information.At the same time,the application of algorithm-recommended mobile new media is also accompanied by potential negative effects,whether the algorithm is neutral,whether the algorithm causes the echo room effect,the algorithm affects the user’s acceptance of diverse views,and the algorithm affects the user’s attitude to public events.Received the attention of academic circles at home and abroad.Therefore,the algorithm recommendation technology and its products are undoubtedly worth the attention of communication scholars.Compared with foreign academic circles,the domestic scientific research on the advantages and disadvantages of algorithm recommendation technology is still at the theoretical level.The use and impact of algorithm recommendation mobile new media also need to be tested,compared and synthesized.Based on this,this research selects the answer text of the topic "Why did you uninstall today’s headlines and vibrato" in the question and answer community as the public research data of the mobile media new context of algorithm recommendation.By analyzing the reasons why users uninstall today’s headlines and vibrato,the mining process and results of algorithm fatigue are excavated and summarized.Use Python crawler to get the answer text,through calculation rooting theory to interpret the main category connotation of algorithm fatigue cause and the core category relationship structure of algorithm fatigue cause,analyze the formation process and result of algorithm fatigue.In addition,a questionnaire survey is used to describe the formation process and results of algorithm fatigue,to obtain algorithm fatigue user behavior and emotion feature data sets,cluster analysis to obtain algorithm fatigue user types,and to analyze and discuss the characteristics of various types of user characteristics.The study found that in the context of algorithm-recommended mobile new media,users have a sense of inferiority due to social comparison,a sense of disgust due to the contrast between virtual and reality,users are alert to the unconscious indulgence caused by algorithm supervision,and users interact with personalized information Overload consumes a lot of time and regrets;the level of algorithm self-confidence of different users and the perception of algorithm transparency affect the generation of user algorithm fatigue;algorithm check results in user dissatisfaction with information quality,algorithm bias affects the acceptance of user diversity views,The homogenization of information brought about by information overload affects the generation of user algorithm fatigue.Therefore,the reasons for users’ enthusiasm for the algorithm-recommended mobile new media to decline or even uninstall and exit mainly include social factors(social comparison,algorithm supervision,personalized information interaction overload),technical factors(algorithm self-confidence,algorithm transparency),information factors(Algorithm check,algorithm bias,information overload).Cluster analysis of user emotion and behavior feature data sets,and classify algorithm fatigue users into algorithm pessimistic users,algorithm active coping users,and algorithm expectation users.Both positive and negative aspects are used to discuss the user stickiness of different algorithmic fatigue users to algorithm-recommended mobile new media. |
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